Data Mining and Data Warehousing:Principles and Practical Techniques
By: Bhatia,Parteek.
Material type:
BookPublisher: United Kingdom Cambridge University Press 2019Description: 477.ISBN: 978-1-108-72774-7.Subject(s): Data MiningDDC classification: 006.312
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BSDU Knowledge Resource Center, Jaipur General Stacks | 006.312 BHA (Browse shelf) | Available | 018035 |
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| 006.31 MIT Machine Learning | 006.31 MIT Machine Learning | 006.31 SHA Understanding Machine Learning:From theory to algorithms | 006.312 BHA Data Mining and Data Warehousing:Principles and Practical Techniques | 006.312 GOS Data Mining and Data Warehousing | 006.312 JAI Big Data & Hadoop | 006.312 MIL Head First Data Analysis |
Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.
Discusses important concepts with their practical implementation using Weka and R language data mining tools
Includes advanced topics such as big data analytics, relational data models and NoSQL that are discussed in detail
Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding
Preface
Acknowledgement
Dedication
1. Beginning with machine learning
2. Introduction to data mining
3. Beginning with Weka and R language
4. Data pre-processing
5. Classification
6. Implementing classification in Weka and R
7. Cluster analysis
8. Implementing clustering with Weka and R
9. Association mining
10. Implementing association mining with Weka and R
11. Web mining and search engine
12. Operational data store and data warehouse
13. Data warehouse schema
14. Online analytical processing
15. Big data and NoSQL
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